Gastos Casa
24 March, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 13/1/2025 | Comida | 67387 | Tami | Supermercado |
| 20/1/2025 | Comida | 21692 | Andrés | gnoccis |
| 20/1/2025 | Comida | 86884 | Tami | Supermercado |
| 21/1/2025 | Comida | 21525 | Andrés | piwen |
| 23/1/2025 | VTR | 21990 | Andrés | NA |
| 25/1/2025 | Diosi | 20000 | Andrés | arena diosi |
| 27/1/2025 | Comida | 71516 | Tami | Supermercado |
| 30/1/2025 | Electricidad | 55000 | Andrés | NA |
| 6/2/2025 | Comida | 52730 | Andrés | supermercado (no cobre el otro de 25k pq muchas son cosas mías) |
| 9/2/2025 | Comida | 12500 | Andrés | NA |
| 17/2/2025 | Comida | 7940 | Andrés | NA |
| 18/2/2025 | Electricidad | 64888 | Andrés | la puse por adelantado para que no se me olvide |
| 18/2/2025 | Comida | 17820 | Tami | Supermercado |
| 23/2/2025 | Comida | 86908 | Tami | Supermercado |
| 27/2/2025 | Comida | 10000 | Andrés | NA |
| 26/2/2025 | Comida | 4620 | Andrés | NA |
| 1/3/2025 | Comida | 2300 | Tami | Supermercado |
| 2/3/2025 | Comida | 102058 | Tami | Supermercado |
| 3/3/2025 | Comida | 9370 | Andrés | NA |
| 9/3/2025 | Comida | 61916 | Tami | Supermercado |
| 11/3/2025 | Comida | 27021 | Andrés | NA |
| 11/3/2025 | Enceres | 13190 | Tami | 40 rollos confort |
| 15/3/2025 | Comida | 78061 | Tami | Supermercado |
| 17/3/2025 | Electricidad | 52458 | Andrés | NA |
| 17/3/2025 | VTR | 22000 | Andrés | NA |
| 21/3/2025 | Agua | 19562 | Andrés | NA |
| 22/3/2025 | Comida | 76766 | Tami | Supermercado |
| 21/3/2025 | Diosi | 18500 | Andrés | antiparasitario |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 9.9612e+08 2 5.0451 0.0066 **
## lag_depvar 2.6285e+11 1 2662.5790 <2e-16 ***
## Residuals 8.1149e+10 822
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1893.199 16350.88 0.1509127
## 2-0 31344.993 23132.310 39557.68 0.0000000
## 2-1 24116.155 19357.799 28874.51 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
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## 693 52725.00 2 56293.86
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## 695 47513.00 2 58625.00
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## 708 98395.14 2 95424.29
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## 727 33690.71 2 32975.00
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## 746 67134.29 2 43139.14
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## 753 56116.43 2 73015.00
## 754 111885.00 2 56116.43
## 755 131425.14 2 111885.00
## 756 136678.00 2 131425.14
## 757 115531.29 2 136678.00
## 758 118310.86 2 115531.29
## 759 117449.43 2 118310.86
## 760 115193.57 2 117449.43
## 761 61025.43 2 115193.57
## 762 43913.86 2 61025.43
## 763 46099.29 2 43913.86
## 764 44524.86 2 46099.29
## 765 42208.71 2 44524.86
## 766 166486.57 2 42208.71
## 767 171565.29 2 166486.57
## 768 200415.71 2 171565.29
## 769 204498.14 2 200415.71
## 770 197558.86 2 204498.14
## 771 195266.57 2 197558.86
## 772 203144.29 2 195266.57
## 773 85493.71 2 203144.29
## 774 74721.57 2 85493.71
## 775 36232.14 2 74721.57
## 776 40161.71 2 36232.14
## 777 40629.86 2 40161.71
## 778 45663.71 2 40629.86
## 779 39252.29 2 45663.71
## 780 39618.57 2 39252.29
## 781 39438.43 2 39618.57
## 782 44650.71 2 39438.43
## 783 38626.71 2 44650.71
## 784 38280.43 2 38626.71
## 785 44134.14 2 38280.43
## 786 47596.43 2 44134.14
## 787 45598.43 2 47596.43
## 788 42564.29 2 45598.43
## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
## 791 50018.43 2 49553.86
## 792 43772.86 2 50018.43
## 793 39235.43 2 43772.86
## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
## 797 34324.14 2 34230.57
## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
## 801 49770.86 2 46646.86
## 802 57339.86 2 49770.86
## 803 59799.14 2 57339.86
## 804 53577.14 2 59799.14
## 805 61775.29 2 53577.14
## 806 70627.86 2 61775.29
## 807 57888.43 2 70627.86
## 808 49960.71 2 57888.43
## 809 42923.71 2 49960.71
## 810 47284.86 2 42923.71
## 811 52284.86 2 47284.86
## 812 50191.00 2 52284.86
## 813 36465.86 2 50191.00
## 814 34525.14 2 36465.86
## 815 43199.14 2 34525.14
## 816 52757.43 2 43199.14
## 817 43200.86 2 52757.43
## 818 36772.29 2 43200.86
## 819 29568.00 2 36772.29
## 820 42362.00 2 29568.00
## 821 42566.29 2 42362.00
## 822 39596.00 2 42566.29
## 823 32925.00 2 39596.00
## 824 43416.57 2 32925.00
## 825 52624.86 2 43416.57
## 826 57733.71 2 52624.86
## 827 54120.57 2 57733.71
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 670 53579.26 22295.877
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 2020.48116 4041.05543 -538.71536 2437.51372 -2970.90422 518.28354
## 8 9 10 11 12 13
## -5656.52837 -1186.92980 -3965.16500 -416.09253 -4938.09570 -1606.66600
## 14 15 16 17 18 19
## -897.00971 380.02161 -3240.86652 -375.30173 -2127.82569 6606.64396
## 20 21 22 23 24 25
## -1529.24912 -1208.05759 1476.01420 -1186.86670 234.61085 1694.75554
## 26 27 28 29 30 31
## -7102.89982 948.89283 8193.26525 416.54373 -15.57946 -2401.98294
## 32 33 34 35 36 37
## 1575.66846 4571.55134 1124.72508 2389.20263 -1870.65801 4606.16498
## 38 39 40 41 42 43
## 4302.71790 -2277.57543 -2982.36760 -1110.05605 -10741.03787 7292.91290
## 44 45 46 47 48 49
## 2558.15319 1366.85080 8104.75423 683.37643 6526.14093 6710.17297
## 50 51 52 53 54 55
## -5888.00770 -4798.61132 -5061.04311 -7928.18141 6132.53675 -4076.08183
## 56 57 58 59 60 61
## -4892.65216 3858.71992 889.18669 -31.22839 142.98549 -4995.83321
## 62 63 64 65 66 67
## 18128.78972 3636.95933 -3650.41224 5922.75321 7340.06223 14633.31987
## 68 69 70 71 72 73
## 1684.10335 -13221.01889 -1309.21202 4641.35641 -4903.47676 -4405.70200
## 74 75 76 77 78 79
## -10496.96339 2470.83855 -5396.83614 1068.25014 -6862.53172 552.58283
## 80 81 82 83 84 85
## -2349.65974 -2688.65273 -3926.28312 -531.17380 2320.46433 3767.07305
## 86 87 88 89 90 91
## 479.45954 -482.51817 198.50580 4303.49109 -1163.13568 1151.11596
## 92 93 94 95 96 97
## -2064.63836 -1043.74764 178.41228 275.44449 -7483.55781 2394.82632
## 98 99 100 101 102 103
## -8600.56318 -2935.68661 -4033.89255 -1730.13175 -1254.66523 3187.48546
## 104 105 106 107 108 109
## -2337.36103 2599.04431 -1154.88653 974.00100 2589.87892 -3152.85769
## 110 111 112 113 114 115
## -4720.54669 -846.30233 1907.37295 11696.28993 -1244.36303 2667.45461
## 116 117 118 119 120 121
## 4261.00539 3499.63554 -1103.75349 -4719.17040 -3724.78813 2320.60817
## 122 123 124 125 126 127
## -1732.64109 1341.15308 8858.50235 844.26688 127.76272 -2523.56732
## 128 129 130 131 132 133
## 2654.04482 7050.77367 1008.49453 -8503.13241 1749.02321 4134.79172
## 134 135 136 137 138 139
## -3166.00138 -1420.34212 -853.94485 -3879.60950 1184.85108 -494.25452
## 140 141 142 143 144 145
## -2912.29724 1720.40882 -1879.68226 -7827.36044 2044.01171 -3476.43436
## 146 147 148 149 150 151
## 2106.35300 -254.64437 1025.57076 -357.47296 1353.89837 1187.61233
## 152 153 154 155 156 157
## 3356.98680 -4862.65413 -1173.46969 -3234.49911 5959.04536 9746.52856
## 158 159 160 161 162 163
## -3647.31761 -4992.85013 3390.85082 -20.39822 2480.36554 -6128.13891
## 164 165 166 167 168 169
## -6962.99427 3943.42647 17176.08429 3395.15459 -633.89905 -2680.63673
## 170 171 172 173 174 175
## -1336.61448 3358.93715 -462.60966 -8309.47567 2635.76314 4094.63606
## 176 177 178 179 180 181
## 390.90973 8515.26592 -9490.67197 -3705.83100 -10976.14842 -11463.74530
## 182 183 184 185 186 187
## 1018.62756 9073.71181 -1659.29727 5699.34090 6318.98758 12913.70252
## 188 189 190 191 192 193
## 8170.86402 -4333.41851 2197.57830 10097.58554 -1926.78358 -2725.45455
## 194 195 196 197 198 199
## -10558.11239 -6628.55168 975.76736 -5492.34663 -10048.64970 5142.39924
## 200 201 202 203 204 205
## -3315.82214 -1956.45344 -1046.60477 6252.01543 9629.13091 309.91971
## 206 207 208 209 210 211
## 2654.99249 2824.45288 5507.40054 12550.23870 -5985.66795 -11583.43375
## 212 213 214 215 216 217
## -5935.95767 -10847.85062 -5320.69578 1287.91759 -13251.98435 16164.55425
## 218 219 220 221 222 223
## 7559.89436 1266.60513 26424.23933 12223.19660 7016.28900 13701.71326
## 224 225 226 227 228 229
## -4253.45894 -2068.41916 3458.61943 42.11070 2434.53559 8696.17657
## 230 231 232 233 234 235
## 5517.69113 -2217.35552 -2128.93284 9133.20096 -11808.81470 -7563.64053
## 236 237 238 239 240 241
## -8809.07580 -10356.73640 2835.90229 1105.46465 -8545.86033 -9227.29431
## 242 243 244 245 246 247
## 8867.95586 -8007.13133 2255.12808 -10538.93066 -4279.04037 1200.25222
## 248 249 250 251 252 253
## 775.08483 -12548.41689 3425.70268 1835.22807 3977.82606 1891.28032
## 254 255 256 257 258 259
## -1409.71567 10889.76119 20611.33461 2890.67773 -4581.67850 3808.43159
## 260 261 262 263 264 265
## -2000.53785 3436.63550 -5156.18966 -11187.25731 -5002.25628 -787.23302
## 266 267 268 269 270 271
## -5453.49615 8520.67477 -4555.65389 3920.62326 -2384.89349 4155.79734
## 272 273 274 275 276 277
## 424.56149 7016.63156 -1712.98110 11727.22855 -4906.65840 1413.05947
## 278 279 280 281 282 283
## -686.97092 7539.06823 -5384.52244 -3044.22336 -11565.48833 -2946.05986
## 284 285 286 287 288 289
## 18384.41794 7471.02442 2405.92579 -959.94875 579.48136 6072.72970
## 290 291 292 293 294 295
## 6545.21580 -19121.36740 -11435.55028 -8386.64733 9421.49266 2804.15716
## 296 297 298 299 300 301
## -1454.13952 27130.65221 9723.87684 4538.74859 9150.63993 2472.92771
## 302 303 304 305 306 307
## -1413.42771 7528.24488 -24674.92495 -3836.78667 -461.94959 -7250.07862
## 308 309 310 311 312 313
## -4230.47225 2686.82933 -9444.67364 -3454.68501 -8401.48397 1372.95264
## 314 315 316 317 318 319
## -3351.30484 1853.90877 -4287.44649 27247.92095 -1027.21980 2991.65288
## 320 321 322 323 324 325
## 10522.67676 5251.96679 32032.72431 4676.55879 -21367.99960 1442.14034
## 326 327 328 329 330 331
## 764.68200 -6804.66379 -2045.18058 -33566.58779 726.83071 -2460.10920
## 332 333 334 335 336 337
## -244.22271 -3320.31564 3941.01546 -598.65909 -7115.28659 -3258.88744
## 338 339 340 341 342 343
## -2327.76706 -7813.09182 3738.44733 -1505.23652 -1873.24211 -1129.29742
## 344 345 346 347 348 349
## 38.72245 337.27728 -1770.25823 -9598.31822 -13335.52412 2221.45109
## 350 351 352 353 354 355
## -4430.77199 -3760.27681 -6078.61704 1662.73618 1278.31999 2631.14064
## 356 357 358 359 360 361
## -3908.71447 -653.73983 533.09580 6858.86429 88.75388 -231.36008
## 362 363 364 365 366 367
## 2386.54793 -2959.19309 -1076.71767 -8940.30481 -4789.36985 -6360.61620
## 368 369 370 371 372 373
## -5077.77076 -7367.76171 4920.05854 245.05932 6983.50618 -7808.54394
## 374 375 376 377 378 379
## -2408.81696 -3530.02658 -2601.16380 -12587.89833 1814.54339 -10740.93249
## 380 381 382 383 384 385
## 5621.41076 9225.16345 2966.41089 -2578.11055 1429.73084 6557.21550
## 386 387 388 389 390 391
## 11191.26205 -6072.30471 -5611.39742 -386.28273 8333.06380 1547.42055
## 392 393 394 395 396 397
## 10947.24474 -10198.98161 2496.76746 425.20120 274.54649 -941.09218
## 398 399 400 401 402 403
## -844.63496 -14763.61033 8315.74124 -1420.29697 -1603.43575 6759.07119
## 404 405 406 407 408 409
## -8183.92248 -1509.65773 -2735.89087 -6009.94473 -3022.66868 -4068.97049
## 410 411 412 413 414 415
## -8892.31252 6030.33564 1501.89840 -7525.54049 -7816.89052 14123.10543
## 416 417 418 419 420 421
## 3642.19504 4292.78129 -8260.46202 -4933.19632 -2769.56753 2661.12487
## 422 423 424 425 426 427
## -14185.50131 -2906.25121 -9207.68991 2936.81279 6876.21825 6432.59297
## 428 429 430 431 432 433
## -4167.39340 -4286.21488 -4872.78078 -1923.73454 -5843.98481 -6739.95760
## 434 435 436 437 438 439
## -6041.97075 -1469.71710 -929.81020 -5064.81935 2502.72811 4737.02769
## 440 441 442 443 444 445
## -5190.86823 -2277.43444 1458.49741 -3970.11452 2712.49763 -6721.20564
## 446 447 448 449 450 451
## -12230.78947 -4587.38630 9577.28265 -2152.50394 4635.61871 -6015.41685
## 452 453 454 455 456 457
## -1248.07322 257.15553 2892.03368 -12420.75163 3263.82481 -6828.44536
## 458 459 460 461 462 463
## 6416.65805 2871.76844 2349.14991 -4017.33268 1935.23988 -176.54719
## 464 465 466 467 468 469
## 1621.77141 -701.69317 3171.07420 -2833.41934 5622.83553 -7148.06211
## 470 471 472 473 474 475
## -3138.03687 -2365.77834 -4815.31511 2862.81711 7647.92539 -6200.90503
## 476 477 478 479 480 481
## 1326.09843 -6344.10467 -2984.82499 1880.51067 -13073.08932 -9850.22273
## 482 483 484 485 486 487
## -1267.03276 -50.59415 -1043.53934 -1428.89372 -9675.46588 11032.99664
## 488 489 490 491 492 493
## 6118.10511 7272.28887 -5617.31041 5207.96637 9110.35919 5834.35311
## 494 495 496 497 498 499
## -13713.57479 -10746.20608 -3578.13235 -1231.65464 -649.37987 -7752.69128
## 500 501 502 503 504 505
## 510.92317 4179.37053 5378.24908 504.88562 -78.73621 -7399.38000
## 506 507 508 509 510 511
## 437.31984 -5186.07150 1711.88853 -1428.30376 -8287.89277 -704.19748
## 512 513 514 515 516 517
## -2780.39664 -689.22474 1226.62308 -9611.91224 -7850.93313 24221.87135
## 518 519 520 521 522 523
## 9656.98134 5672.17792 -5563.00936 2596.18392 16808.43496 11199.71794
## 524 525 526 527 528 529
## -24458.79429 -5263.44722 -3914.03594 4407.03853 -540.27274 -11283.96126
## 530 531 532 533 534 535
## 4254.18021 13752.29694 -5189.84457 4182.83845 5347.87018 -2016.94191
## 536 537 538 539 540 541
## -4756.11255 -7267.54687 -2263.79148 8167.57804 -61.01162 -8328.39423
## 542 543 544 545 546 547
## 1662.53016 -761.13733 206.60688 -11192.58375 -11181.10229 1959.48348
## 548 549 550 551 552 553
## 6906.24363 -1448.63794 707.39917 -7857.14069 8455.21214 758.31665
## 554 555 556 557 558 559
## -12098.39497 9052.07238 8505.38052 -90.06630 4664.02007 -3781.84532
## 560 561 562 563 564 565
## 13917.10645 21252.86813 -6791.29685 -9967.33350 6536.30542 -33.21725
## 566 567 568 569 570 571
## 3202.15957 -7635.75409 -17528.58425 6517.07107 6263.42869 1708.10125
## 572 573 574 575 576 577
## 2900.48744 1564.19551 -2372.87936 14522.30667 -9898.05401 -6449.60137
## 578 579 580 581 582 583
## 8533.86884 2648.63528 -6762.13425 7322.44607 -4012.81993 -2969.10527
## 584 585 586 587 588 589
## 15523.00213 -14737.56941 8251.61167 -136.83336 -6415.15165 -928.36073
## 590 591 592 593 594 595
## 82.97180 -10821.40589 1671.01494 -7279.22696 2958.53973 8741.60043
## 596 597 598 599 600 601
## -7661.99934 5718.61244 2577.49064 6687.73905 -3383.31520 5971.81571
## 602 603 604 605 606 607
## -8497.16731 2092.25133 1099.64655 2965.05302 1312.41311 212.25026
## 608 609 610 611 612 613
## -5993.61946 7916.89449 -1370.41135 -2752.14983 -3618.02756 -8377.53099
## 614 615 616 617 618 619
## 11840.88775 4772.71242 -9494.14260 11484.48922 5865.94569 -5774.48068
## 620 621 622 623 624 625
## 26185.26560 -13088.47188 -6981.88796 2986.97574 -4336.63596 -10750.29599
## 626 627 628 629 630 631
## 11178.33089 -21794.65144 -2497.45404 8595.69741 11020.76370 -1707.59524
## 632 633 634 635 636 637
## 33136.57998 -6846.60873 5493.24990 5165.27464 -2510.15258 -5567.41487
## 638 639 640 641 642 643
## -2134.51585 -12612.21320 -2376.68878 -2012.73830 -2640.98162 -2971.33241
## 644 645 646 647 648 649
## 1709.40162 4316.43726 16833.58697 18364.46783 637.32364 4550.50679
## 650 651 652 653 654 655
## 10367.41065 19882.49715 428.66192 -28359.23003 -1526.03821 -2462.53954
## 656 657 658 659 660 661
## 1712.30274 -3347.14430 -10760.54520 1562.67399 4119.42863 -1126.01305
## 662 663 664 665 666 667
## 12917.47885 1207.83492 1661.64602 -11843.50444 1265.19174 1070.60482
## 668 669 670 671 672 673
## -5284.39901 -7511.57295 1986.91195 -3798.76206 2595.48497 -3466.52283
## 674 675 676 677 678 679
## -9416.43180 -8364.32973 -3021.56831 127.78644 2793.78422 646.15113
## 680 681 682 683 684 685
## -3900.08827 -1876.25509 -1386.14315 -8311.64064 4595.22833 -2313.72749
## 686 687 688 689 690 691
## -1468.26381 516.55937 10777.15477 9743.47612 10494.61789 -9812.16805
## 692 693 694 695 696 697
## -3671.82426 -3245.76625 5773.98899 -10495.56011 -7994.74172 -8677.25857
## 698 699 700 701 702 703
## -6324.58650 -4781.48805 3043.12329 -4454.92645 -1947.34441 4171.12315
## 704 705 706 707 708 709
## 31041.34006 9412.47683 23336.33905 1564.01583 8218.08733 22820.65171
## 710 711 712 713 714 715
## 6458.31662 -18295.83274 4754.99309 -5507.69115 -156.83272 426.09851
## 716 717 718 719 720 721
## -17318.66660 -5304.45237 3297.68099 -3053.00034 -13016.14715 4249.61151
## 722 723 724 725 726 727
## -5590.32579 710.84254 -3968.03660 -12479.25619 1341.99614 -1895.61964
## 728 729 730 731 732 733
## -9806.69763 17244.08176 1737.41902 -2762.24343 5677.11804 -8672.59394
## 734 735 736 737 738 739
## -759.97711 8101.57234 -15393.86823 -5943.77511 7377.81415 -4821.03784
## 740 741 742 743 744 745
## 126.48895 1792.21798 -1993.39042 -5204.99226 6378.19312 -6314.37951
## 746 747 748 749 750 751
## 22662.85588 7777.24105 -1999.97004 -7338.26076 23372.06425 -4347.15154
## 752 753 754 755 756 757
## 1345.80318 -14471.30638 56069.33486 26858.77510 15030.40407 -10708.15236
## 758 759 760 761 762 763
## 10557.04911 7265.82798 5762.99798 -46433.16285 -16193.06368 950.63097
## 764 765 766 767 768 769
## -2534.21373 -3474.05275 122828.48584 19268.36107 43679.17637 22541.68935
## 770 771 772 773 774 775
## 12033.70435 15807.47063 25689.01138 -98847.94958 -6774.56719 -35847.41005
## 776 777 778 779 780 781
## 1728.11354 -1238.82116 3385.80382 -7426.02567 -1455.12001 -1955.45550
## 782 783 784 785 786 787
## 3414.30405 -7166.07214 -2246.41280 3910.01090 2255.20867 -2769.38600
## 788 789 790 791 792 793
## -4056.95545 1730.23064 2844.57549 -60.49345 -6712.17504 -5789.96949
## 794 795 796 797 798 799
## -1153.95558 -1269.84015 -7824.05338 -2359.76248 -3274.13039 -2671.47188
## 800 801 802 803 804 805
## 10718.35168 2233.12075 7071.24221 2914.00434 -5457.80698 8179.36477
## 806 807 808 809 810 811
## 9865.44055 -10612.55951 -7403.96390 -7510.86633 3001.74653 4189.40612
## 812 813 814 815 816 817
## -2275.25534 -14170.03023 -4112.76177 6257.73471 8233.54908 -9678.50165
## 818 819 820 821 822 823
## -7753.09234 -9337.77249 9753.93214 -1225.79625 -4374.66054 -8449.15301
## 824 825 826 827
## 7873.94554 7910.91011 4970.24425 -3108.86159
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17248.80 20097.94 24354.86 24072.63 26427.62 23758.43 24475.24 19704.07
## 10 11 12 13 14 15 16 17
## 19440.45 16781.38 17559.38 14286.52 14337.72 15002.84 16700.58 15019.44
## 18 19 20 21 22 23 24 25
## 16054.83 15427.93 22515.25 21598.63 21078.13 22969.44 22294.96 22947.96
## 26 27 28 29 30 31 32 33
## 24795.19 18719.39 20446.73 28289.46 28347.15 28019.84 25647.62 27051.02
## 34 35 36 37 38 39 40 41
## 30896.70 31245.37 32655.52 30164.41 34140.28 37350.58 34404.65 31213.34
## 42 43 44 45 46 47 48 49
## 30060.32 20633.37 28157.28 30595.43 31685.39 38528.19 38022.43 42687.83
## 50 51 52 53 54 55 56 57
## 46927.01 39619.90 34184.61 29203.90 22343.61 28637.94 25216.22 21511.28
## 58 59 60 61 62 63 64 65
## 25922.67 27183.09 27480.30 27892.40 23760.50 40363.18 42208.41 37451.10
## 66 67 68 69 70 71 72 73
## 41660.94 46579.97 57255.47 55267.88 40500.93 38005.07 41025.05 35321.27
## 74 75 76 77 78 79 80 81
## 30770.39 21467.45 24671.12 20594.04 22681.53 17573.56 19590.37 18816.37
## 82 83 84 85 86 87 88 89
## 17843.43 15911.03 17189.68 20800.21 25220.97 26211.52 26236.49 26853.65
## 90 91 92 93 94 95 96 97
## 30981.56 29811.31 30811.35 28874.46 28073.73 28442.13 28848.99 22422.03
## 98 99 100 101 102 103 104 105
## 25439.13 18464.83 17320.18 15359.56 15659.52 16337.37 20813.08 19895.96
## 106 107 108 109 110 111 112 113
## 23409.46 23199.28 24876.55 27755.29 25251.69 21692.73 21968.34 24616.42
## 114 115 116 117 118 119 120 121
## 35488.36 33679.97 35518.71 38519.08 40476.32 38163.17 32980.65 29319.53
## 122 123 124 125 126 127 128 129
## 31403.78 29682.56 30864.93 38469.88 38112.09 37173.00 34034.38 35816.80
## 130 131 132 133 134 135 136 137
## 41218.36 40658.28 31853.98 33119.64 36311.57 32719.77 31105.94 30190.32
## 138 139 140 141 142 143 144 145
## 26745.01 28160.40 27929.87 25614.59 27640.40 26264.22 19861.99 22894.58
## 146 147 148 149 150 151 152 153
## 20719.79 23698.93 24239.29 25830.76 26012.96 27668.24 28969.87 32004.08
## 154 155 156 157 158 159 160 161
## 27471.18 26733.64 24287.24 30185.33 41667.75 39996.85 37360.01 42383.68
## 162 163 164 165 166 167 168 169
## 43793.21 47211.42 42674.28 37978.29 43407.20 59720.42 61934.04 60347.07
## 170 171 172 173 174 175 176 177
## 57170.61 55568.78 58273.18 57296.62 49583.52 52408.94 56154.09 56190.31
## 178 179 180 181 182 183 184 185
## 63323.96 53819.83 50568.58 41371.03 32904.66 36415.29 46525.58 45981.23
## 186 187 188 189 190 191 192 193
## 51938.01 57686.87 68477.14 73763.56 67453.99 67647.56 74722.64 70396.17
## 194 195 196 197 198 199 200 201
## 65915.97 55152.55 49178.66 50603.92 46195.65 38359.17 44788.25 43014.45
## 202 203 204 205 206 207 208 209
## 42652.18 43130.84 49929.44 58824.65 58454.01 60179.98 61836.89 65630.62
## 210 211 212 213 214 215 216 217
## 75103.53 67181.01 55362.10 49967.28 40957.55 37913.23 41028.98 31042.45
## 218 219 220 221 222 223 224 225
## 48027.39 55353.11 56255.62 79036.37 86536.43 88541.00 96137.46 87082.28
## 226 227 228 229 230 231 232 233
## 81076.67 80658.32 77306.04 76466.97 81207.17 82572.36 77004.08 72213.80
## 234 235 236 237 238 239 240 241
## 77871.24 64510.07 56541.22 48486.45 40092.38 44287.11 46441.29 39887.58
## 242 243 244 245 246 247 248 249
## 33562.90 43852.27 38095.30 42033.64 34292.33 32997.32 36655.06 39480.85
## 250 251 252 253 254 255 256 257
## 30304.15 36246.20 40050.17 45248.43 47968.57 47460.81 57768.67 75277.61
## 258 259 260 261 262 263 264 265
## 75092.54 68398.71 69881.54 66099.79 67546.90 61300.40 50567.83 46592.52
## 266 267 268 269 270 271 272 273
## 46802.07 42906.18 51716.23 47986.81 52136.32 50251.63 54321.72 54617.94
## 274 275 276 277 278 279 280 281
## 60639.41 58272.06 67951.52 61872.23 62082.40 60430.36 66177.09 59903.37
## 282 283 284 285 286 287 288 289
## 56464.92 46010.20 44405.87 61649.69 67183.50 67593.23 65009.09 64095.84
## 290 291 292 293 294 295 296 297
## 68099.50 72012.37 52996.12 43091.50 37098.51 47426.84 50670.85 49784.20
## 298 299 300 301 302 303 304 305
## 73996.84 79946.25 80614.36 85229.93 83427.28 78454.18 81923.35 56805.22
## 306 307 308 309 310 311 312 313
## 53063.81 52743.36 46529.33 43736.88 47342.67 39889.83 38611.06 33168.90
## 314 315 316 317 318 319 320 321
## 36956.02 36136.81 39970.88 37953.94 63757.79 61597.49 63222.18 71225.75
## 322 323 324 325 326 327 328 329
## 73614.70 99113.73 97490.29 73304.00 72101.03 70457.24 62403.47 59523.73
## 330 331 332 333 334 335 336 337
## 29451.60 33141.68 33581.51 35903.03 35243.41 41014.37 42090.72 37335.03
## 338 339 340 341 342 343 344 345
## 36548.91 36675.66 31991.41 37994.52 38658.38 38917.01 39793.42 41580.58
## 346 347 348 349 350 351 352 353
## 43403.83 43155.32 36095.10 26656.41 32004.77 30864.99 30454.76 28069.55
## 354 355 356 357 358 359 360 361
## 32751.68 36508.57 40975.29 39163.03 40424.19 42564.14 49964.53 50515.50
## 362 363 364 365 366 367 368 369
## 50717.31 53182.19 50663.86 50108.02 42748.08 39942.90 36117.20 33894.33
## 370 371 372 373 374 375 376 377
## 29949.37 37242.37 39530.92 47421.97 41389.39 40836.17 39372.45 38904.90
## 378 379 380 381 382 383 384 385
## 29766.17 34367.50 27414.30 35639.41 45979.73 49547.68 47819.84 49812.93
## 386 387 388 389 390 391 392 393
## 56037.45 65529.59 58736.11 53200.43 52928.94 60313.72 60837.47 69512.27
## 394 395 396 397 398 399 400 401
## 58610.23 60178.23 59738.02 59221.52 57707.35 56468.04 43217.26 51809.01
## 402 403 404 405 406 407 408 409
## 50808.72 49774.21 56180.07 48717.23 48027.89 46353.37 42027.53 40857.40
## 410 411 412 413 414 415 416 417
## 38919.88 33009.81 40888.24 43816.68 38485.18 33569.89 48452.23 52299.79
## 418 419 420 421 422 423 424 425
## 56231.89 48695.62 45016.28 43691.30 47280.36 35691.11 35420.12 29674.76
## 426 427 428 429 430 431 432 433
## 35268.64 43602.26 50499.39 47262.50 44329.07 41252.02 41140.13 37615.39
## 434 435 436 437 438 439 440 441
## 33750.97 30983.00 32560.24 34410.96 32414.13 37283.83 43493.87 40243.86
## 442 443 444 445 446 447 448 449
## 39949.65 42958.26 40842.79 44835.21 40078.65 31104.39 29941.00 41306.22
## 450 451 452 453 454 455 456 457
## 40987.52 46642.85 42275.79 42625.70 44247.39 47968.32 37835.18 42688.02
## 458 459 460 461 462 463 464 465
## 38107.91 45682.52 49205.14 51827.62 48554.76 50897.26 51098.94 52847.26
## 466 467 468 469 470 471 472 473
## 52344.50 55290.42 52616.74 57671.63 50926.61 48535.78 47120.89 43742.75
## 474 475 476 477 478 479 480 481
## 47501.65 54970.48 49393.33 51097.82 45882.82 44260.63 47095.66 36502.08
## 482 483 484 485 486 487 488 489
## 30058.89 31929.59 34628.25 36119.32 37085.89 30722.00 43261.47 49926.57
## 490 491 492 493 494 495 496 497
## 56761.88 51469.46 56306.07 63945.36 67759.57 54005.78 44576.70 42600.23
## 498 499 500 501 502 503 504 505
## 42923.67 43715.41 38198.08 40598.77 45904.18 51589.97 52300.16 52410.81
## 506 507 508 509 510 511 512 513
## 46108.11 47449.07 43705.54 46463.02 46128.46 39839.63 40971.54 40146.08
## 514 515 516 517 518 519 520 521
## 41252.52 43894.48 36729.36 32005.27 55912.45 64079.11 67734.72 61108.96
## 522 523 524 525 526 527 528 529
## 62449.42 76045.00 83026.79 57958.73 52825.04 49516.96 53899.13 53405.10
## 530 531 532 533 534 535 536 537
## 43581.53 48576.99 61246.70 55763.59 59163.70 63154.37 60204.83 55231.98
## 538 539 540 541 542 543 544 545
## 48689.51 47344.42 55287.30 55037.54 47592.18 49817.42 49643.96 50338.30
## 546 547 548 549 550 551 552 553
## 40980.53 32810.37 37155.33 45277.78 45074.60 46781.71 40787.22 49806.68
## 554 555 556 557 558 559 560 561
## 50962.82 40734.64 50282.48 58150.92 57515.41 61115.70 56879.89 68648.85
## 562 563 564 565 566 567 568 569
## 85349.44 75433.33 63988.69 68411.07 66534.13 67721.61 59285.58 43263.21
## 570 571 572 573 574 575 576 577
## 50276.86 56186.18 57369.80 59446.80 60094.31 57218.69 69474.05 58839.89
## 578 579 580 581 582 583 584 585
## 52558.42 60165.36 61670.42 54759.55 61030.53 56603.53 53646.00 67225.71
## 586 587 588 589 590 591 592 593
## 52643.96 59993.40 59085.15 52802.93 52107.60 52383.83 43093.13 45891.94
## 594 595 596 597 598 599 600 601
## 40514.60 44763.40 53532.86 46859.39 52722.51 55101.98 60775.03 56930.47
## 602 603 604 605 606 607 608 609
## 61747.60 53310.32 55191.64 55968.52 58278.30 58852.75 58393.19 52566.53
## 610 611 612 613 614 615 616 617
## 59633.13 57691.86 54787.03 51490.82 44448.83 55967.14 59857.29 50786.37
## 618 619 620 621 622 623 624 625
## 61195.63 65383.48 58868.73 81111.76 66224.17 58548.17 60552.49 55902.58
## 626 627 628 629 630 631 632 633
## 46231.24 56946.08 37488.88 37349.02 46923.95 57413.88 55457.13 84206.04
## 634 635 636 637 638 639 640 641
## 74385.46 76587.73 78226.15 72948.84 65663.09 62295.07 50191.69 48558.88
## 642 643 644 645 646 647 648 649
## 47449.70 45930.90 44314.46 46993.13 51613.70 66594.82 81028.96 78150.35
## 650 651 652 653 654 655 656 657
## 79054.73 84930.22 98384.05 93139.09 63388.90 60838.97 57791.27 58776.57
## 658 659 660 661 662 663 664 665
## 55215.12 45621.33 48007.29 52328.01 51519.66 63089.31 62966.93 63256.65
## 666 667 668 669 670 671 672 673
## 51704.24 53064.68 54083.83 49419.43 43395.09 46432.05 44029.23 47518.38
## 674 675 676 677 678 679 680 681
## 45269.29 38102.04 32756.43 32753.93 35504.79 40239.99 42501.95 40505.11
## 682 683 684 685 686 687 688 689
## 40528.71 40977.78 35316.34 41650.01 41147.12 41446.58 43443.42 54158.38
## 690 691 692 693 694 695 696 697
## 62621.38 70676.03 59965.68 55970.77 52851.01 58008.56 48294.88 41989.69
## 698 699 700 701 702 703 704 705
## 35881.30 32598.20 31077.16 36587.50 34849.92 35523.02 41459.95 70138.67
## 706 707 708 709 710 711 712 713
## 76301.38 93860.27 90177.06 92774.06 107809.25 106649.12 83995.86 84343.41
## 714 715 716 717 718 719 720 721
## 75675.98 72776.76 70751.95 53470.17 48865.46 52359.86 49863.00 38970.96
## 722 723 724 725 726 727 728 729
## 44542.61 40811.44 43058.04 40931.83 31633.00 35586.33 36211.98 29843.35
## 730 731 732 733 734 735 736 737
## 47922.87 50171.96 48204.60 53862.17 46263.83 46538.57 54525.15 40967.92
## 738 739 740 741 742 743 744 745
## 37377.61 45884.32 42656.80 44160.35 46930.82 46043.42 42460.24 49453.52
## 746 747 748 749 750 751 752 753
## 44471.43 65447.04 70770.68 66877.55 58807.79 78599.29 71669.20 70587.73
## 754 755 756 757 758 759 760 761
## 55815.67 104566.37 121647.60 126239.44 107753.81 110183.60 109430.57 107458.59
## 762 763 764 765 766 767 768 769
## 60106.92 45148.65 47059.07 45682.77 43658.09 152296.92 156736.54 181956.45
## 770 771 772 773 774 775 776 777
## 185525.15 179459.10 177455.27 184341.66 81496.14 72079.55 38433.60 41868.68
## 778 779 780 781 782 783 784 785
## 42277.91 46678.31 41073.69 41393.88 41236.41 45792.79 40526.84 40224.13
## 786 787 788 789 790 791 792 793
## 45341.22 48367.81 46621.24 43968.91 46709.28 50078.92 50485.03 45025.40
## 794 795 796 797 798 799 800 801
## 41058.96 41644.27 42054.62 36683.91 36765.70 36037.90 35928.51 47537.74
## 802 803 804 805 806 807 808 809
## 50268.61 56885.14 59034.95 53595.92 60762.42 68500.99 57364.68 50434.58
## 810 811 812 813 814 815 816 817
## 44283.11 48095.45 52466.26 50635.89 38637.90 36941.41 44523.88 52879.36
## 818 819 820 821 822 823 824 825
## 44525.38 38905.77 32608.07 43792.08 43970.66 41374.15 35542.63 44713.95
## 826 827
## 52763.47 57229.43
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8104
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.045113 0.7769986 3.921777
## t2* 2662.578983 166.2596891 880.853249
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.097096 5.000461 13.28527
## 2 lag_depvar 1610.509920 2713.203396 4493.78602Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Mar 24 00:52:36 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Mar 24 00:52:45 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Mar 24 00:52:54 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Mar 24 00:53:04 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Mar 24 00:53:13 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Mar 24 00:53:22 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Mar 24 00:53:32 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Mar 24 00:53:41 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Mar 24 00:53:50 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Mar 24 00:54:00 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 0.0000 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 258.8850 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 59.9440 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 0.0000 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.0000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 0.0000 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 36.9995 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 10.9950 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.0000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.0000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 366.8235 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2655, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2655 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.3## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.3## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.3##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-04-09 00:04:58 sería de: 26.676 pesos// Percentil 95% más alto proyectado: 35.051,67
Según TimeGPT: La proyección de la UF a 298 días más 2026-02-01 sería de: 40.421,82 pesos// Percentil 80% más alto proyectado: 40.735,26 pesos// Percentil 95% más alto proyectado: 41.080,7
Según prophet: La proyección de la UF a 298 días más 2026-02-01 sería de: 39.289 pesos// Percentil 95% más alto proyectado: 42.622
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26293.96 | 26328.60 |
| Lo.80 | 26425.51 | 26491.43 |
| Point.Forecast | 26675.83 | 26799.01 |
| Hi.80 | 31453.43 | 32108.70 |
| Hi.95 | 34319.77 | 34919.48 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,1,1)
##
## Coefficients:
## ar1 ma1
## 0.4188 -0.9485
## s.e. 0.1294 0.0592
##
## sigma^2 = 37557: log likelihood = -481.1
## AIC=968.2 AICc=968.55 BIC=975.03paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.4055 562.8354 14.9430
## s.e. 0.1106 328.3026 10.1052
##
## sigma^2 = 35910: log likelihood = -484.98
## AIC=977.96 AICc=978.55 BIC=987.12forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 669.6808 | 643.7799 | 629.0613 |
| Lo.80 | 810.3198 | 795.5792 | 723.3464 |
| Point.Forecast | 1075.9932 | 1083.7927 | 948.1711 |
| Hi.80 | 1341.6666 | 1375.4911 | 1241.8993 |
| Hi.95 | 1482.3056 | 1529.9070 | 1432.1579 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.5 Rcpp_1.0.14
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.12
## [10] tidytext_0.4.2 DT_0.33 janitor_2.2.1
## [13] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [16] xts_0.14.1 forecast_8.23.0 wordcloud_2.6
## [19] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-16
## [22] NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4
## [25] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.4
## [28] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [31] gsynth_1.2.1 sjPlot_2.8.17 lattice_0.22-6
## [34] GGally_2.2.1 ggplot2_3.5.1 gridExtra_2.3
## [37] plotrix_3.8-4 sparklyr_1.9.0 httr_1.4.7
## [40] readxl_1.4.5 zoo_1.8-13 stringr_1.5.1
## [43] stringi_1.8.4 DataExplorer_0.8.3 data.table_1.17.0
## [46] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [49] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 datawizard_1.0.1
## [4] httr2_1.1.1 lifecycle_1.0.4 StanHeaders_2.32.10
## [7] doParallel_1.0.17 globals_0.16.3 vroom_1.6.5
## [10] MASS_7.3-60.2 insight_1.1.0 crosstalk_1.2.1
## [13] magrittr_2.0.3 sass_0.4.9 rmarkdown_2.29
## [16] jquerylib_0.1.4 yaml_2.3.10 fracdiff_1.5-3
## [19] doRNG_1.8.6.1 askpass_1.2.1 pkgbuild_1.4.6
## [22] DBI_1.2.3 abind_1.4-8 quadprog_1.5-8
## [25] nnet_7.3-19 rappdirs_0.3.3 sandwich_3.1-1
## [28] inline_0.3.21 tokenizers_0.3.0 listenv_0.9.1
## [31] anytime_0.3.11 performance_0.13.0 spatial_7.3-17
## [34] parallelly_1.42.0 codetools_0.2-20 xml2_1.3.8
## [37] tidyselect_1.2.1 ggeffects_2.2.1 farver_2.1.2
## [40] urca_1.3-4 its.analysis_1.6.0 matrixStats_1.5.0
## [43] stats4_4.4.0 jsonlite_1.9.1 ellipsis_0.3.2
## [46] Formula_1.2-5 iterators_1.0.14 systemfonts_1.2.1
## [49] foreach_1.5.2 tools_4.4.0 glue_1.8.0
## [52] xfun_0.51 TTR_0.24.4 ggfortify_0.4.17
## [55] loo_2.8.0 withr_3.0.2 timeSeries_4041.111
## [58] fastmap_1.2.0 boot_1.3-30 openssl_2.3.2
## [61] caTools_1.18.3 digest_0.6.37 timechange_0.3.0
## [64] R6_2.6.1 lfe_3.1.1 colorspace_2.1-1
## [67] networkD3_0.4 gtools_3.9.5 generics_0.1.3
## [70] htmlwidgets_1.6.4 ggstats_0.9.0 pkgconfig_2.0.3
## [73] gtable_0.3.6 timeDate_4041.110 lmtest_0.9-40
## [76] selectr_0.4-2 janeaustenr_1.0.0 htmltools_0.5.8.1
## [79] carData_3.0-5 tseries_0.10-58 snakecase_0.11.1
## [82] knitr_1.50 rstudioapi_0.17.1 tzdb_0.5.0
## [85] uuid_1.2-1 nlme_3.1-164 curl_6.2.1
## [88] cachem_1.1.0 sjlabelled_1.2.0 KernSmooth_2.23-22
## [91] parallel_4.4.0 fBasics_4041.97 pillar_1.10.1
## [94] vctrs_0.6.5 gplots_3.2.0 slam_0.1-55
## [97] car_3.1-3 dbplyr_2.5.0 xtable_1.8-4
## [100] evaluate_1.0.3 mvtnorm_1.3-3 cli_3.6.4
## [103] compiler_4.4.0 crayon_1.5.3 rngtools_1.5.2
## [106] future.apply_1.11.3 labeling_0.4.3 sjmisc_2.8.10
## [109] rstan_2.32.7 QuickJSR_1.6.0 viridisLite_0.4.2
## [112] assertthat_0.2.1 munsell_0.5.1 lazyeval_0.2.2
## [115] Matrix_1.7-0 sjstats_0.19.0 hms_1.1.3
## [118] bit64_4.6.0-1 future_1.34.0 nixtlar_0.6.2
## [121] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.10
## [124] bslib_0.9.0 quantmod_0.4.26 bit_4.6.0#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))